Chung Yuan Christian University

About: Chung Yuan Christian University is a education organization based out in Taoyuan City, Taiwan. It is known for research contribution in the topics: Membrane & Fuzzy logic. The organization has 9819 authors who have published 11623 publications receiving 213139 citations. The organization is also known as: Tiong-gôan-tāi-ha̍k & CYCU.

Multifunction biquadratic filters using current conveyors

Abstract: Some novel multifunction biquadratic filters with voltage gain, each of which employs four current conveyors, two grounded capacitors, and three-five resistors, are presented. Each proposed circuit offers the following advantageous features: realization of different biquadratic filter signals from the same configuration, no requirements for component matching conditions, employment of only two grounded capacitors which are ideal for integration, orthogonal control of /spl omega//sub 0/ and Q and low sensitivities.

Major depression detection from EEG signals using kernel eigen-filter-bank common spatial patterns

Abstract: Major depressive disorder (MDD) has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG) signals and a robust spectral-spatial EEG feature extractor called kernel eigen-filter-bank common spatial pattern (KEFB-CSP). The KEFB-CSP first filters the multi-channel raw EEG signals into a set of frequency sub-bands covering the range from theta to gamma bands, then spatially transforms the EEG signals of each sub-band from the original sensor space to a new space where the new signals (i.e., CSPs) are optimal for the classification between MDD and healthy controls, and finally applies the kernel principal component analysis (kernel PCA) to transform the vector containing the CSPs from all frequency sub-bands to a lower-dimensional feature vector called KEFB-CSP. Twelve patients with MDD and twelve healthy controls participated in this study, and from each participant we collected 54 resting-state EEGs of 6 s length (5 min and 24 s in total). Our results show that the proposed KEFB-CSP outperforms other EEG features including the powers of EEG frequency bands, and fractal dimension, which had been widely applied in previous EEG-based depression detection studies. The results also reveal that the 8 electrodes from the temporal areas gave higher accuracies than other scalp areas. The KEFB-CSP was able to achieve an average EEG classification accuracy of 81.23% in single-trial analysis when only the 8-electrode EEGs of the temporal area and a support vector machine (SVM) classifier were used. We also designed a voting-based leave-one-participant-out procedure to test the participant-independent individual classification accuracy. The voting-based results show that the mean classification accuracy of about 80% can be achieved by the KEFP-CSP feature and the SVM classifier with only several trials, and this level of accuracy seems to become stable as more trials (i.e., <7 trials) are used. These findings therefore suggest that the proposed method has a great potential for developing an efficient (required only a few 6-s EEG signals from the 8 electrodes over the temporal) and effective (~80% classification accuracy) EEG-based brain-computer interface (BCI) system which may, in the future, help psychiatrists provide individualized and effective treatments for MDD patients.

Quantitative analysis of quantity and distribution of epicardial adipose tissue surrounding the left atrium in patients with atrial fibrillation and effect of recurrence after ablation.

Abstract: Epicardial adipose tissue (EAT) contains ganglionated plexuses and adipocytes that can affect the pathogenesis of atrial fibrillation (AF). The aim of this study was to quantify the EAT surrounding the left atrium (LA) and correlate it with occurrence of AF and outcome after catheter ablation. EAT was evaluated using 64-slice multidetector computed tomography in 68 patients with AF and 34 controls. EAT volume was acquired by semiautomatically tracing axial images from the pulmonary artery to the coronary sinus. Topographic distribution of EAT was assessed by dividing the periatrial space into 8 equal regions. EAT volume significantly increased in patients with AF than in controls (29.9 ± 12.1 vs 20.2 ± 6.5 cm(3), p <0.001). Most EAT was located in regions (1) within the superior vena cava, right pulmonary artery, and right-sided roof of the LA (29.8%), (2) within the aortic root, pulmonary trunk, and left atrial appendage (26.5%), and (3) between the left inferior pulmonary vein and left atrioventricular groove (18.1%). Baseline variables were analyzed in patients with (n = 24) and without (n = 44) AF recurrence after ablation. The recurrent group showed significantly increased EAT (35.2 ± 12.5 vs 26.8 ± 11.1 cm(3), p = 0.007). Multivariate analysis revealed that EAT was an independent predictor of AF recurrence after ablation (p = 0.038). In conclusion, EAT of LA was increased in patients with AF. Large clusters of EAT were observed adjacent to the anterior roof, left atrial appendage, and lateral mitral isthmus. Abundance of EAT was independently related to AF recurrence after ablation.